tbuckley/cabot-search huggingface dataset:
Abstract MP47: Sleep Disordered Breathing is Adversely Associated With Cardiac Autonomic Modulation in Adolescents
Authors: Julio Fernandez-Mendoza, Alexis M Isely, Natasha Morales-Ghinaglia, Fan He, Susan Calhoun, Jason Liao, Alexandros Vgontzas, Duanping Liao, Edward O Bixler
Journal: Circulation (2023)

Introduction: Sleep disordered breathing (SDB), as measured by the apnea/hypopnea index (AHI), is a known risk factor for cardiovascular disease (CVD) in adults. Although SDB is prevalent in pediatric populations, there has been less focus on SDB as a primary risk factor for CVD in youth. The recent AHA scientific statement on SDB and CVD in children and adolescents suggests that, in order to better understand the long-term CVD-related risk associated with SDB in youth, additional longitudinal studies incorporating noninvasive markers of CVD are needed. The current study aimed to fill this important gap. Hypothesis: We hypothesized that SDB is associated with decreased HRV in adolescents. Methods: We studied 421 subjects from the Penn State Child Cohort who were 8.7 (1.7) years old at baseline and were followed-up as adolescents 16.5 (2.3) years old. We estimated the AHI from 9-hour, in-lab polysomnography (PSG), and frequency and time-domain heart rate variability (HRV) indices from 24-h Holter EKG monitoring immediately following PSG. We stratified 24-h HRV data into daytime and nighttime periods. Linear regression analyses examined the association between AHI with 24-h HRV indices, while adjusting for age, sex, race/ethnicity, BMI percentile, and metabolic syndrome (MetS) score in adolescence as well as AHI and nighttime HRV indices in childhood. Results: While there were no significant associations between AHI and daytime (p=0.233) or nighttime (p=0.147) HR, there were significant associations with frequency and time-domain HRV indices. A higher AHI was associated with lower Log-HF, Log-LF, SDNN and RMSSD, yet higher LF/HF, for both daytime and nighttime periods (all p&lt;0.05). For example, for each additional apnea or hypopnea per hour of sleep there was a decrease of -1.74ms (0.73) and -1.65ms (0.77) in SDNN during the daytime (p=0.018) and nighttime (p=0.033), respectively, even after adjusting for all covariates including MetS, childhood AHI and SDNN. More specifically, the 24-h SDNN of adolescents with an AHI≥5, indicative of moderate-to-severe SDB, was 10.9ms (4.4) lower than that of adolescents with an AHI=0 (p=0.014). Conclusions: SDB is associated with impaired cardiac autonomic balance in adolescence independently of MetS, prior SDB and prior HRV status. These data further support the role of SDB as an independent risk factor for CVD early in life.
Abstract P448: Trends and Predictors of Statin Initiation Among Veterans With Familial Hypercholesterolemia
Authors: Saadia Qazi, Laura Tarko, Yuk-lam Ho, David Gagnon, Ariela R Orkaby, Luc Djousse, John M Gaziano, Kelly Cho, Peter W Wilson
Journal: Circulation (2023)

Introduction: Familial hypercholesterolemia (FH), defined as LDL-C ≥190mg/dL, is a strong cardiovascular disease (CVD) risk factor. The AHA/ACC guidelines recommend high intensity statins for primary prevention irrespective of global risk assessment. We sought to characterize trends and predictors of statin initiation among Veterans with FH in the Veterans Health Administration (VHA). Methods: Inclusion criteria consisted of care at the VHA (2002-2014), age ≥21 years, baseline LDL-C ≥190 mg/dL, no prevalent CVD, no prior statin use, and statin initiation within 90 days of LDL-C measurement. We defined baseline LDL-C in quartiles: Q1=190-195, Q2=196-202, Q3=203-215, and Q4 &gt;215 (mg/dL). Statin intensity was defined as average intensity over one year following baseline LDL-C measurement. Multivariable logistic regression models were constructed to determine the odds of any statin initiation and high intensity statin initiation. Results: Over 12 years, 165,110 Veterans (age=52.9±11.9, men=91%; white race=74%) were included. Statins were initiated in 88,878 patients (overall 54%, 62% in 2014). Overall, 29,533 Veterans were on high intensity statins (overall 33%, 20% in 2002, 50% in 2014). In multivariable-adjusted logistic regression models, significant predictors of statin initiation were age, white race, diabetes hypertension, smoking, baseline LDL-C, and recent calendar year. Among those initiated on high intensity statins, the same predictors were significant; however, African Americans were more likely to be started on a high intensity statin (Table). Conclusions: Among Veterans with FH, the odds of statin initiation are higher among older, Caucasian, hypertensive, diabetic, and smoking veterans. The odds of statin prescription have increased over time with a higher proportion receiving high intensity statins more recently. These data reflect the VHA changes in clinical practice concordant with ACC/AHA guidelines.
Abstract MP28: The Association Between the Saliva Microbiome and Biomarkers of Cardiometabolic Disease in ORIGINS
Authors: Bruno Bohn, Sumith Roy, Clarisse Marotz, Rob Knight, Michael Rosenbaum, Bruce Paster, Melana Yuzefpolskaya, Paolo C Colombo, Panos Papapanou, Moise Desvarieux, David R Jacobs, Ryan Demmer
Journal: Circulation (2023)

Introduction: The present study sought to characterize taxonomic and functional characteristics of the saliva microbiome and their cross-sectional association with cardiometabolic biomarkers. Hypothesis: Features of the saliva microbiome will be associated with cardiometabolic risk. Methods: Saliva microbiome composition from 265 ORIGINS participants was defined with 16S rRNA sequencing (Illumina, DADA2). Functional composition (KEGG orthologs; KOs) was inferred with PICRUSt2. Alpha Diversity was computed with the Shannon Index. Blood pressure was defined as normal (SBP&lt;120, DBP&lt;80, and no antihypertensive medication) vs elevated/high (hypertension: SBP≥120, DBP≥80 and/or antihypertensive medication). Prediabetes was defined as 5.7≤HbA1≤6.4% or 100≤fasting plasma glucose≤125 mg/dL. HOMA-IR was calculated from insulin and glucose. HOMA-IR and CRP were dichotomized as high/low using thresholds of 3 mg/L and 1.9, respectively. Multivariable multinomial regression with cross-validation was conducted for each feature (species, KOs) and outcome, ranking regression coefficients from most positively to most inversely associated features. Microbiota and KO ratios (Micro-R, KO-R) were computed as the log-ratio of the sum of abundances of taxa/KOs in the top vs bottom tertiles of cross-validated ranks, such that higher ratios are potentially adverse. Adjusted multivariable logistic regressions produced ORs summarizing the association between Shannon, Micro-R, and KO-R and each cardiometabolic outcome. Results: Participants were 71% female, 22% Black, 40% Hispanic, and 18% white, with mean age of 33±10 years. Prevalence of hypertension was 36%, prediabetes was 17%, high CRP was 23%, and high HOMA-IR was 25%. Estimated ORs for the association between microbiome metrics and cardiometabolic outcomes are summarized in Figure 1. Conclusion: Microbiome scores based on specific taxa or KOs are associated with hypertension, prediabetes, and increased inflammation and insulin resistance.
Abstract P212: Plasma Metabolomics of Dietary Intake of Protein-Rich Foods and Kidney Disease Progression in Children
Authors: Xuyuehe Ren, Jingsha Chen, Alison G Abraham, Yunwen Xu, Aisha Siewe, Bradley A Warady, Eugene P Rhee, Susan L Furth, Josef Coresh, Michelle Denburg, Casey M Rebholz
Journal: Circulation (2023)

Introduction: There is inconsistent evidence on the efficacy of a low-protein diet for patients with chronic kidney disease (CKD) and there is a lack of objective biomarkers of dietary intake. The purpose of this study was to identify plasma metabolites associated with dietary intake of protein among children with CKD and to assess whether protein-related metabolites are associated with CKD progression. Methods: We conducted non-targeted metabolomics using plasma samples from 484 Chronic Kidney Disease in Children (CKiD) participants. Multivariable linear regression estimated the cross-sectional association between 949 known, non-drug metabolites and dietary intake of dairy, nuts and beans, red and processed meat, fish, chicken, and eggs, adjusting for demographic, clinical, and dietary covariates. Cox proportional hazards regression models assessed the prospective association between protein-related metabolites and CKD progression defined as kidney replacement therapy or 50% eGFR reduction, adjusting for demographic and clinical covariates. We used a false discovery rate &lt;0.05 as the statistical significance threshold to account for multiple comparisons. Results: Fifty-seven unique metabolites were significantly associated with dietary protein intake (dairy: n=21; nuts and beans: n=13; red and processed meat: n=20; fish: n=2; chicken: n=3; eggs: n=0). Among them, ten metabolites were significantly associated with CKD progression (TABLE) , including one amino acid, one cofactor and vitamin, four lipids, two nucleotides, one peptide, and one xenobiotic. 1-(1-enyl-palmitoyl)-2-oleoyl-GPE (P-16:0/18:1) was positively associated with dietary intake of red and processed meat and CKD progression, and 3-ureidopropionate was inversely associated with dietary intake of red and processed meat and CKD progression. Conclusion: Untargeted plasma metabolomic profiling revealed metabolites associated with dietary intake of protein and CKD progression in a pediatric population.
Abstract P208: Metabolomic Profiles Associated With Blood Pressure Reduction in Response to the DASH and DASH-Sodium Trials
Authors: Hyunju Kim, Lawrence J Appel, Alice H Lichtenstein, Kari Wong, Nilanjan Chatterjee, Eugene P Rhee, Casey M Rebholz
Journal: Circulation (2023)

Introduction: The Dietary Approaches to Stop Hypertension (DASH) diet significantly reduced blood pressure (BP) in the DASH and DASH-Sodium trials, but the underlying mechanisms are unclear. Hypothesis: We hypothesized that certain metabolites will be associated with BP reductions. Methods: Metabolomic profiling was conducted at the end of the diet interventions for participants randomly assigned to the DASH or control diet in multicenter, controlled feeding studies: DASH (N=219) and DASH-Sodium trials (N=395). Using multivariable linear regression models to examine the association between metabolites and BP change, we tested for interactions between diet and metabolites for the following comparisons: 1) DASH vs. control diets in the DASH trial, 2) DASH-high sodium vs. control-high sodium diets in the DASH-Sodium trial, and 3) DASH-low sodium vs. control-high sodium diets in the DASH-Sodium trial. Pathway overrepresentation analysis was constructed for significant metabolites. Results: Identified were 65 significant interactions [DASH=12; DASH-high sodium=35; DASH-low sodium=18]. Most amino acids (16/19), cofactors and vitamins (15/15), xenobiotics (9/13), peptides (4/5), and a nucleotide (1/1) were higher in participants consuming the DASH diets vs. the control diets. These metabolites were associated with BP reductions in participants consuming the DASH diets, but not control diets ( Figure ). Many lipid metabolites (10/12) were lower in participants consuming the DASH vs. the control diets. Lipid concentrations were associated with BP elevations in participants consuming the DASH diets and with BP reductions in participants consuming the control diets. Metabolites associated with tocopherol metabolism, ceramides, and pantothenate and CoA metabolism were overrepresented. Conclusions: Using data from randomized feeding studies, we identified metabolites that were associated with BP lowering. These metabolites highlight the pathways through which DASH diet reduced BP.
Abstract P437: Testing a Clinical Decision Support Tool to Promote Physical Activity
Authors: Margaret M McCarthy, Adam Szerencsy, Leslie Taza-Rocano, Stephanie Hopkins, Gail D Melkus, Devin Mann, Allison Vorderstrasse, Stuart Katz
Journal: Circulation (2023)

Introduction: Physical activity (PA) is an essential component of health, yet it is not regularly assessed, nor are patients routinely counseled on PA as recommended by the AHA. The aim of this study was to evaluate the acceptability and clinical utility of incorporating an electronic clinical decision support (CDS) tool and remote patient monitoring to assess, promote and monitor PA in a preventive cardiology clinic. Methods: The CDS tool was pilot-tested in the Epic electronic health record (EHR) from July 2021-June 2022. Patients answered 3 questions about routine PA in their patient portal prior to an office visit. The CDS alerted the provider to counsel the patient if their PA level was &lt; 50% of recommended PA. These patients were invited to participate in remote patient monitoring for PA using a Fitbit connected to their EHR. The Practical, Robust Implementation and Sustainability Model (PRISM) was used to guide and evaluate the implementation. Qualitative feedback was collected from providers and patients. Results: Over 12 months, patients answered a 3-question PA screener 33%-43 % per month and the CDS tool fired a range of 79-125 times per month. The HCP opened and signed the CDS tool between 3.2% to 21.6% monthly; it was acknowledged (e.g., ‘PA not appropriate for this patient at this time’) between 1-22% per month. Changes to the CDS during the pilot included removing the CDS tool from the medical assistant’s workflow to prevent them from taking action on it, and revising the options for acknowledgements based on provider feedback. Patients (n=59) were enrolled in 12 weeks of remote PA monitoring with 4 patients lost to follow-up, and 58% able to sync their Fitbit to Epic EHR using written directions. Feedback from the providers indicated they found the CDS easy to use but wanted additional information as to why patients were not reaching recommended PA (e.g., boredom). Patients wanted to add more detail about their PA in the patient portal, and spoke about needing motivation and more frequent reminders about being active. All were willing to engage in remote monitoring again. Conclusion: Implementing the electronic PA assessment, counseling, and remote monitoring is feasible in a preventive cardiology clinic. However, use of the PA screener by patients and the CDS tool by providers was low and strategies are needed to improve its uptake. Patients may also need more guidance in connecting an activity tracker to the EHR for remote monitoring.
Abstract P314: Social Determinants of Cardiovascular Risk Factors Among African Immigrants in New York City
Authors: Ednah N Madu, Oluwabunmi Ogungbe, Hadiza Osuji, GRACE OGIEHOR-ENOMA, Chiamaka A Madu, Aichata Bamba, Yvonne Commodore-Mensah
Journal: Circulation (2023)

Background: African immigrants (AIs) constitute a highly diverse and rapidly growing population in the US, particularly in New York City. Yet, their cardiovascular health remains understudied. We examined the prevalence of cardiovascular disease (CVD) risk factors and identified predictors of increased CVD risk among AIs residing in the five New York City (NYC) boroughs. Objective: To examine the relationship between social determinants (marital status, education, employment, and health insurance) and CVD risk factors among AIs living in NYC. Methods: This cross-sectional study was conducted among AIs ≥30 years residing in NYC. Outcomes of interest were self-reported CVD risk factors assessed individually (hypertension, diabetes, hyperlipidemia, overweight/obesity, and poor sleep). Using logistic regression analyses, we examined the associations between social determinants and individual CVD risk factors, adjusting for age and sex. Results: Of 215 participants, the mean age was 54 (SD: 11.7) years, and 70% were female. The majority (93%) had ≥1 of the 6 CVD risk factors. Smoking was least prevalent, and overweight/obesity was most prevalent (76%); with 42%, and 18% having a prior diagnosis of hypertension and diabetes, respectively. Those who were employed had ≥ bachelor's education were less likely to have hyperlipidemia (adjusted Odds Ratio [aOR]: 0.35, 95%CI: 0.15-0.86, or overweight/obesity (aOR: 0.31, 95%CI: 1.11-0.88); and twice (aOR: 2.14, 95%CI: 1.11-4.15), more likely to report poor sleep. However, being married and having health insurance were associated with a 3-fold (aOR: 3.08, 95%CI: 1.12-8.50) and 4-fold (aOR: 4.07, 95%CI: 1.12-14.73) higher likelihood of being overweight/obese. Conclusions: The prevalence of CVD risk factors among AIs was particularly high. Culturally-appropriate interventions and policies to reduce CVD risk factors are highly recommended for this population.
Abstract P313: Characteristics and Patterns of a Middle-Class Australian Cohort With Cardiometabolic Multimorbidity: A Cross-Sectional Study
Authors: Belinda J Parmenter, Kelly McLeod, Matthew Jones, Claire Burley, Xiaoqi Feng
Journal: Circulation (2023)

Introduction: Prevalence rates of cardiometabolic disease are rising, placing a significant burden on healthcare sectors, with cardiovascular disease (CVD) and type 2 diabetes contributing the most burden and being overweight the most contributing risk factor. With an ageing population, unfavourable risk factor trends and the challenges associated with CVD, we aimed to analyse a group of middle-class Australian adults for patterns in body composition, fitness and function. Methods: Data collected from adults attending a health clinic in eastern Sydney were analysed. Data were self-reported to or measured by an allied health clinician during initial consultations. Conditions reported included those contributing to the Australian disease burden in adults 45 years and over in 2018. Cardiometabolic multimorbidity was defined as the presence of two or more conditions simultaneously. Simple linear and stepwise regressions were used to assess potential relationships. Results: Data from 533 people (mean age 66.6±12.1 yrs; 67% female) were analysed. In total, 418 had a biological cardiometabolic risk factor or diagnosed CVD, of which 85.2% satisfied the criteria for cardiometabolic multimorbidity (73% female; 62% male), with most reporting 2-3 conditions. The most reported was coronary heart disease (64.4%), followed by hypertension (34.6%), type 2 diabetes (12.9%), cerebrovascular (5.1%), chronic kidney (1%) and peripheral artery (1%) disease. There was no relationship between the number of conditions and key outcomes, except for balance (r=0.12; p=0.03). There was a significant relationship between age and greater systolic blood pressure (SBP) (r=0.26; p&lt;0.001), a larger waist (r=0.11; p=0.03) and poorer 6-minute walk (r=-0.38; p&lt;0.001), muscle strength (r=-0.34; p&lt;0.001) and balance (r=-0.30; p&lt;0.001). A high SBP was significantly related to a higher body mass index and waist and lower body strength, with waist and lower body strength being the strongest predictors of higher SBP (p=0.005). A higher waist and lower body strength were significantly related to a poorer 6-minute walk, together with age, accounting for 41% of the variance in performance (p&lt;0.001). Conclusion: Three in 4 Australian adults in this cohort had cardiometabolic multimorbidity, with a greater prevalence in females. Cardiometabolic multimorbidity was unrelated to key body composition parameters or fitness test performance. However, those that were older had a higher waist measurement and poorer lower body strength and performance in the 6-minute walk. Those with higher SBP also had a higher waist and lower body strength. With these fitness measures strongly related to cardiovascular mortality, more older adults need to participate in evidence-based healthy lifestyle education programs to help improve overall body composition, fitness and function, thereby reducing CVD.
Abstract MP31: Serum Metabolite Signatures for Metabolic Healthy and Unhealthy Obesity: The Bogalusa Heart Study
Authors: Changwei Li, Zhijie Huang, Xiao Sun, Yang Pan, Jing Chen, Tanika N Kelly, Shengxu Li, Wei Chen, Lydia A Bazzano, Jiang He
Journal: Circulation (2023)

Introduction: Mechanisms for metabolically healthy obesity (MHO) are not well studied. Methods: We performed cross-sectional analyses among 1,261 participants of the Bogalusa Heart Study 2013-2016 survey. A total of 887 known serum metabolites were profiled using an untargeted approach. Participants who met all the following five criteria were considered metabolically healthy, otherwise, unhealthy: 1) SBP &lt;130 mm Hg and DBP &lt;85 mm Hg; 2) fasting glucose &lt;100 mg/dl; 3) triglycerides &lt;150 mg/dl; 4) HDL ≥40 mg/dl in men and ≥50 mg/dl in women; and 5) not taking medications for hypertension, diabetes, or dyslipidemia. We tested associations of metabolites with metabolic health status, controlling for age, sex, race, education, smoking, drinking, and kidney function. Results: Among 648 obese participants (BMI≥30 kg/m 2 ), 66 (10.2%) had MHO. After Bonferroni correction, 26 metabolites differed between MHO and metabolically unhealthy obesity (MUO) groups. Amino acids serine , glycine, and histidine , fatty acid N-linoleoylserine , 4 sphingolipids, and 3 plasmalogens were significantly higher among MHO individuals. These metabolites are functionally relevant to metabolic health ( Table ). Carbohydrates mannose , lactate, and pyruvate , urate , amino acids glutamate , 1-carboxyethylvaline , 1-carboxyethylleucine , 3-methyl-2-oxovalerate , and 3-methyl-2-oxobutyrate , PE(18:0/18:2(9Z,12Z)) , and 5 diacylglycerols were significantly higher in the MUO group. These metabolites are mostly food additives or reduce gut microbiota diversity ( Table ). Furthermore, histidine was associated with metabolic health only among overweight and obese participants. Odds ratios of being metabolically healthy per SD increase of histidine among normal weight, overweight, and obese participants were 1.05 (95% CI: 0.83-1.32), 1.54 (95% CI: 1.10-2.14), and 2.12 (95% CI: 1.56-2.89), respectively (P for group difference=1.41E-4). Conclusion: We identified potential intervention targets to improve metabolic health in obesity.